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In [1]:
import pandas as pd
import urllib
import numpy as np
import urllib.request
import re
from textblob import TextBlob
%run lib.py
In [2]:
#name="Legally%20Blonde"
#name="aboutmary"
#name="10Things"
name="magnolia"
#name="Friday%20The%2013th"
#name="Ghost%20Ship"
#name="Juno"
#name="Reservoir+Dogs"
#name="shawshank"
#name="Sixth%20Sense,%20The"
#name="sunset_bld_3_21_49"
#name="Titanic"
#name="toy_story"
#name="trainspotting"
#name="transformers"
#name="the-truman-show_shooting"
#name="batman_production"
In [3]:
ext="html"
txtfiles=["Ghost%20Ship", "Legally%20Blonde", "Friday%20The%2013th", "Juno", "Reservoir+Dogs", "Sixth%20Sense,%20The", "Titanic"]
if name in txtfiles:
    ext="txt"
fp = urllib.request.urlopen("http://www.dailyscript.com/scripts/"+name+"."+ext)
mybytes = fp.read()

mystr = mybytes.decode("utf8", "ignore")
fp.close()
liston=mystr.split("\n")
liston=[s.replace('\r', '') for s in liston]
liston=[re.sub('<[^<]+?>', '', text) for text in liston]
In [4]:
if name=="shawshank":
    liston=[i.replace("\t", "    ") for i in liston]
In [5]:
char=""
script=[]
charintro='                                 '
endofdialogue='          '
dialoguepre='                    '
newscenepre='          '
charintro=''
endofdialogue=''
dialoguepre=''
newscenepre=''
i=45
print("Characters")
i, charintro=nextbigchunk(liston, i)
print("Adverbs")
i, adverb=nextbigchunk(liston, i, adverbs=True)
print("Dialogues")
i, dialoguepre=nextbigchunk(liston, i)
print("New Scene:")
i, newscenepre=nextbigchunk(liston, i)

if newscenepre=="X":
    i=100
    i, newscenepre=nextbigchunk(liston, i)
    if name=="aboutmary":
        newscenepre=" ".join(["" for i in range(56)])
    if len(newscenepre)==len(charintro):
        newscenepre="X"
    

endofdialogue=newscenepre
    

scene=1
for s in liston:
    if s[0:len(charintro)]==charintro and s[len(charintro)]!=" " and s.strip()[0]!="(" and s.strip()[len(s.strip())-1]!=")":
        #print("Charatcer*****")
        char=s[len(charintro):]
        new=dict()
        new['char']=char.strip()
        new['dialogue']=""
        new['scene']=scene
        new['adverb']=""
    if s==endofdialogue or s.replace(" ", "")=="":
        if char!="":
            char=""
            script.append(new)
    if char!="" and s[0:len(dialoguepre)]==dialoguepre and s[len(dialoguepre)]!=" ":
        #print("Dialogue******")
        if new['dialogue']!="":
            new['dialogue']=new['dialogue']+" "
        new['dialogue']=new['dialogue']+s[len(dialoguepre):]
    if char!="" and ((s[0:len(adverb)]==adverb and s[len(adverb)]!=" ") or (len(s)>1 and s.strip()[0]=="(" and s.strip()[len(s.strip())-1]==")" )):
        if new['adverb']!="":
            new['adverb']=new['adverb']+" "
        new['adverb']=new['adverb']+s[len(adverb):]
    if s[0:len(newscenepre)]==newscenepre and len(s)>len(newscenepre) and ( s.isupper()) and s[len(newscenepre)]!=" ":
        scene=scene+1
Characters
                                magnolia
                                NARRATOR
                                NARRATOR
                                NARRATOR
                                NARRATOR
                                NARRATOR
Adverbs
Dialogues
                      In the New York Herald, November 26,
                      year 1911, there is an account of the
                      hanging of three men --
                      ...they died for the murder of
                      Sir Edmund William Godfrey --
                      -- Husband, Father, Pharmacist and all
New Scene:
     a P.T. Anderson picture                             11/10/98
     a Joanne Sellar/Ghoulardi Film Company production
     
     
     
     
In [6]:
pd.DataFrame(script).to_csv(name+'.csv', index=None)
pd.DataFrame(script)
Out[6]:
adverb char dialogue scene
0 magnolia 1
1 NARRATOR In the New York Herald, November 26, year 1911... 2
2 NARRATOR ...they died for the murder of Sir Edmund Will... 2
3 NARRATOR -- Husband, Father, Pharmacist and all around ... 2
4 NARRATOR Greenberry Hill, London. Population as listed. 3
5 NARRATOR He was murdered by three vagrants whose motive... 5
6 NARRATOR ...Joseph Green..... 5
7 NARRATOR ...Stanley Berry.... 5
8 NARRATOR ...and Nigel Hill... 5
9 NARRATOR Green, Berry and Hill. 7
10 NARRATOR ...And I Would Like To Think This Was Only A M... 7
11 NARRATOR As reported in the Reno Gazzette, June of 1983... 9
12 NARRATOR --- the water that it took to contain the fire -- 10
13 NARRATOR -- and a scuba diver named Delmer Darion. 12
14 NARRATOR Employee of the Peppermill Hotel and Casino, R... 15
15 NARRATOR -- well liked and well regarded as a physical,... 16
16 NARRATOR -- as reported by the coroner, Delmer died of ... 21
17 NARRATOR ...volunteer firefighter, estranged father of ... 24
18 NARRATOR -- added to this, Mr. Hansen's tortured life m... 26
19 CRAIG HANSEN ...oh God...fuck...I'm sorry...I'm sorry... 27
20 NARRATOR The weight of the guilt and the measure of coi... 27
21 CRAIG HANSEN ...forgive me... 27
22 NARRATOR And I Am Trying To Think This Was All Only A M... 29
23 NARRATOR The tale told at a 1961 awards dinner for the ... 32
24 NARRATOR Seventeen year old Sydney Barringer. In the ci... 33
25 NARRATOR The coroner ruled that the unsuccessful suicid... 33
26 NARRATOR The suicide was confirmed by a note, left in t... 34
27 NARRATOR At the same time young Sydney stood on the le... 35
28 NARRATOR The neighbors heard, as they usually did, the... 36
29 NARRATOR -- and it was not uncommon for them to threat... 37
... ... ... ... ...
1493 DIXON We gotta get his money so we can get outta her... 382
1494 WORM That idea is over now. We're not gonna do tha... 382
1495 (to Stanley) DIXON DADDY, FUCK, DADDY, DON'T GET MAD AT ME. DON'T... 382
1496 WORM I'm not mad, son, I will not be mad at you an... 382
1497 DIXON DAD. 382
1498 DIXON I - just - thought - that - I - didn't want - ... 382
1499 WORM It's ok, boy. 382
1500 MUSIC/KERMIT THE FROG "It's not that easy bein' green... Having to s... 383
1501 DONNIE My teeff...my teeef.... 385
1502 JIM KURRING YOU'RE OK...you're gonna be ok.... 385
1503 NARRATOR And there is the account of the hanging of thr... 390
1504 NARRATOR There are stories of coincidence and chance an... 391
1505 NARRATOR ...and we generally say, "Well if that was in... 392
1506 DOCTOR Are you with us? Linda? Is it Linda? 394
1507 NARRATOR Someone's so and so meet someone else's so and... 395
1508 NARRATOR And it is in the humble opinion of this narrat... 398
1509 STANLEY Dad...Dad. 399
1510 STANLEY You have to be nicer to me, Dad. 399
1511 RICK Go to bed. 399
1512 STANLEY I think that you have to be nicer to me. 399
1513 RICK Go to bed. 399
1514 NARRATOR ...and so it goes and so it goes and the book... 400
1515 MARCIE I killed him. I killed my husband. He hit my... 401
1516 DONNIE I know that I did a thtupid thing. Tho-thtupid... 402
1517 DONNIE I really do hath love to give, I juth don't kn... 402
1518 JIM KURRING ...these security systems can be a real joke. ... 403
1519 DONNIE ....ohh-thur-I-thur-thill.... 403
1520 JIM KURRING You guys make alotta money, huh? 403
1521 (beat) JIM KURRING ...alot of people think this is just a job tha... 405
1522 END. 406

1523 rows × 4 columns

In [7]:
magnolia=pd.read_csv(name+'.csv')
stopwords = getstopwords()
In [8]:
removedchars=["'S VOICE", "'S WHISPER VOICE", " GATOR"]
for s in removedchars:
    magnolia['char']=magnolia['char'].apply(lambda x: x.replace(s, ""))
i=0
scenes=dict()
for s in magnolia.iterrows():
    scenes[s[1]['scene']]=[]
for s in magnolia.iterrows():
    scenes[s[1]['scene']].append(s[1]['char'])
for s in magnolia.iterrows():
    scenes[s[1]['scene']]=list(set(scenes[s[1]['scene']]))
In [9]:
characters=[]
for s in scenes:
    for k in scenes[s]:
        characters.append(k)
characters=list(set(characters))
appearances=dict()
for s in characters:
    appearances[s]=0
for s in magnolia.iterrows():
    appearances[s[1]['char']]=appearances[s[1]['char']]+1
In [10]:
a=pd.DataFrame(appearances, index=[i for i in range(len(appearances))])
In [11]:
finalcharacters=[]
for s in pd.DataFrame(a.transpose()[0].sort_values(0, ascending=False))[0:10].iterrows():
    finalcharacters.append(s[0])
In [12]:
finalcharacters
file=open(name+"_nodes.csv", "w")
couplesappearances=dict()
for s in finalcharacters:
    file.write(";")
    file.write(s)
file.write("\n")
for s in finalcharacters:
    newlist=[]
    for f in finalcharacters:
        newlist.append(0)
        couplesappearances[f+"_"+s]=0
    j=0
    for f in finalcharacters:
        for p in scenes:
            if f in scenes[p] and s in scenes[p] and f!=s and finalcharacters.index(f)<finalcharacters.index(s): 
                long=len(magnolia[magnolia["scene"]==p])
                newlist[j]=newlist[j]+long
                couplesappearances[f+"_"+s]=couplesappearances[f+"_"+s]+long
        j=j+1
    file.write(s)
    for f in newlist:
        file.write(";")
        file.write(str(f))
    file.write("\n")
file.close()
In [13]:
a=pd.DataFrame(couplesappearances, index=[i for i in range(len(couplesappearances))])
finalcouples=[]
for s in pd.DataFrame(a.transpose()[0].sort_values(0, ascending=False))[0:4].iterrows():
    finalcouples.append(s[0])
In [14]:
file=open(name+"_finalcharacters.csv", "w")
for s in finalcharacters:
    file.write(s+"\n")
file.close()
file=open(name+"_finalcouples.csv", "w")
for s in finalcouples:
    file.write(s+"\n")
file.close()
In [15]:
importantchars=[]
for char in appearances:
    if appearances[char]>10:
        importantchars.append(char)
In [16]:
file=open(name+"_sentiment_overtime_individual.csv", "w")
file2=open(name+"_sentiment_overtime_individualminsmaxs.csv", "w")

for k in finalcharacters:
    print(k)
    dd=getdialogue(magnolia, k, k, scenes)
    dd=[str(d) for d in dd]
    polarities, subjectivities=getsentiment(dd)
    %matplotlib inline
    import matplotlib.pyplot as plt
    moveda=maverage(polarities, dd, .99)
    plt.plot(moveda)
    i=0
    for s in moveda:
        file.write(k+","+str(float(i)/len(moveda))+", "+str(s)+"\n")
        i=i+1
    plt.ylabel('polarities')
    plt.show()
    file2.write(k+"| MIN| "+dd[moveda.index(np.min(moveda))]+"\n")
    file2.write(k+"| MAX| "+dd[moveda.index(np.max(moveda))]+"\n")
    print("MIN: "+dd[moveda.index(np.min(moveda))])
    print("\n")
    print("MAX: "+dd[moveda.index(np.max(moveda))])
    
file.close()
file2.close()

file=open(name+"_sentiment_overtime_couples.csv", "w")
file2=open(name+"_sentiment_overtime_couplesminsmaxs.csv", "w")

for k in finalcouples:
    print(k)
    liston=k.split("_")
    dd=getdialogue(magnolia, liston[0], liston[1], scenes)
    dd=[str(d) for d in dd]
    polarities, subjectivities=getsentiment(dd)
    %matplotlib inline
    import matplotlib.pyplot as plt
    moveda=maverage(polarities, dd, .99)
    plt.plot(moveda)
    i=0
    for s in moveda:
        file.write(k+","+str(float(i)/len(moveda))+", "+str(s)+"\n")
        i=i+1
    plt.ylabel('polarities')
    plt.show()
    file2.write(k+"| MIN| "+dd[moveda.index(np.min(moveda))]+"\n")
    file2.write(k+"| MAX| "+dd[moveda.index(np.max(moveda))]+"\n")
    print("MIN: "+dd[moveda.index(np.min(moveda))])
    print("\n")
    print("MAX: "+dd[moveda.index(np.max(moveda))])
    
file.close()
file2.close()
JIM KURRING
MIN: You mind if I check things back here? 


MAX: YOU'RE OK...you're gonna be ok....
JIMMY
MIN: She went crazy.  She went crazy, Rose. 


MAX: Imagine you are attending a jam session of classical composers and they have  each done an arrangment of the classic  favorite, "Whispering."  Here are three  variations on the theme, as three classic  composer's might have written it -- you are to name the composer.  The First: 
CLAUDIA
MIN: I'm sorry. 


MAX: Did you ever go out with someone and just....lie....question after question, maybe you're trying to  make yourself look cool or better  than you are or whatever, or smarter  or cooler and you just -- not really lie, but maybe you just don't say everything --
FRANK
MIN: If you feel, made to feel like you need them, like -- like you can't live if you're without them or you need, what?  They're pussy?  They're love? Fuck that.  Self Sufficient, gents.  That's the truth. What you are -- we are -- you need them  for what?  To fucking make you a piece of snot rag?  A puppett?  huh?  Hear them bitch and moan? bitch and moan --  and we're taught one thing -- go the other way -- there is No Excuse I will give you, I'm not gonna apologize -- I'm not gonna  apologize for my NEED my DESIRE...my, the  things that I need as a man to feel comfortable... You understand?  You understand?  You need to say something, "my mommy hit me or  daddy hit me or didn't let me play soccer,  so now I make mistakes, cause a that -- something, so now I piss and shit on it and do this." Bullshit.  I'm sorry. ok. yeah. no. fuck.  go.  fuck. alright. go make a new mistake. maybe not, I dunno...fuck.... 


MAX: I wouldn't want that to be misunderstood: My enrollment was totally unoffical because I was, sadly, unable to afford tuition up  there.  But there were three wonderful men who were kind enough to let me sit in on their classes, and they're names are:  Macready, Horn and Langtree among others. I was completely independent financially, and like I said: One Sad Sack A Shit.  So what we're looking at here is a true rags to riches story and I think that's  what most people respond to in "Seduce," And At The End Of The Day? Hey -- it may not  even be about picking up chicks and sticking your cock in it -- it's about finding What You Can Be In This World.  Defining It.  Controling It and  saying: I will take what is mine.  You just happen  to get a blow job out of it, then hey-what-the-fuck- why-not?  he.he.he.
PHIL
MIN: You wanna call him on the phone? We can call him, I can dial the  phone if you can remember the number -- 


MAX: Thank you, Chad, and good luck to you and your mother -- 
STANLEY
MIN: I think that you have to be nicer to me.


MAX: I'm fine. I'm fine, I just wanna keep playing --
DONNIE
MIN: My teeff...my teeef....


MAX: My name is Donnie Smith and I have lot's of love to give. 
EARL
MIN: No, no, the grade...the grade that you're in? 


MAX: "...it's not going to stop 'till you wise up..."
LINDA
MIN: listen...listen to me now, Phil:  I'm sorry, sorry I slapped your face.  ...because I don't know what I'm doing... ...I don't know how to do this, y'know?  You understand?  y'know?  I...I'm...I do things  and I fuck up and I fucked up....forgive me, ok? Can you just...


MAX: I'm listening.  I'm getting better. 
NARRATOR
MIN: -- added to this, Mr. Hansen's tortured life met before with Delmer Darion just two nights previous --


MAX: So Fay Barringer was charged with the  murder of her son and Sydney Barringer  noted as an accomplice in his own death...
JIM KURRING_CLAUDIA
MIN: You mind if I check things back here? 


MAX: ok. 
JIMMY_STANLEY
MIN: I don't mean to cry, I'm sorry. 


MAX: Imagine you are attending a jam session of classical composers and they have  each done an arrangment of the classic  favorite, "Whispering."  Here are three  variations on the theme, as three classic  composer's might have written it -- you are to name the composer.  The First: 
PHIL_EARL
MIN: -- it's not him. it's not him. He's the fuckin' asshole...Phil..c'mere... 


MAX: ...ah...maybe...yeah...she's a good one... 
FRANK_PHIL
MIN: When they put me on hold, to  talk to you...they play the tapes.  I mean: I'd seen the commercials and heard about you, but I'd never heard the tapes ....


MAX: I just...he was...but I gave him,  I just had to give him a small dose of  liquid morphine.  He hasn't been able to swallow the morphine pills so we now,  I just had to go to the liquid morphine... For the pain, you understand? 
In [17]:
for key, val in scenes.items():
    for s in scenes[key]:
        new="INSCENE_"+scenes[key][0]
        scenes[key].remove(scenes[key][0])
        scenes[key].append(new)
In [18]:
magnolia.dropna(subset=['dialogue'])
1
Out[18]:
1
In [19]:
baskets=[]
spchars=["\"", "'", ".", ",", "-"]
attributes=["?", "!"]
for s in magnolia.iterrows():
    if type(s[1]['dialogue'])!=float and  len(s[1]['dialogue'])>0:
        new=[]
        for k in scenes[s[1]['scene']]:
            new.append(k)
        new.append("SPEAKING_"+s[1]['char'])
        for k in s[1]['dialogue'].split(" "):
            ko=k
            for t in spchars:
                ko=ko.replace(t, "")
            for t in attributes:
                if ko.find(t)>=0:
                    new.append(t)
                    ko=ko.replace(t, "")
            if len(ko)>0:
                new.append(ko.lower())
        new=list(set(new))
        baskets.append(new)
In [20]:
baskets2=[]
basketslist=[]
for k in baskets:
    new=dict()
    new2=[]
    for t in k:
        if t not in stopwords:
            new[t]=1
            new2.append(t)
    baskets2.append(new)
    basketslist.append(new2)
In [21]:
baskets2=pd.DataFrame(baskets2)
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules
baskets2=baskets2.fillna(0)
baskets2.to_csv(name+'_basket.csv')
In [22]:
frequent_itemsets = apriori(baskets2, min_support=5/len(baskets2), use_colnames=True)
rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1)
In [23]:
rules['one_lower']=[int(alllower(i) or alllower(j)) for i, j in zip(rules['antecedants'], rules['consequents'])]
In [24]:
rules['both_lower']=[int(alllower(i) and alllower(j)) for i, j in zip(rules['antecedants'], rules['consequents'])]
In [25]:
rules.to_csv(name+'_rules.csv', index=None)

Analisis de Sentimiento (Pelicula & Personaje)

Score por Pelicula

Titulo
.
magnolia
Numero de Palabras/Tokens en el texto original
Palabras Distintas
2436
Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 4.527863 12.1%
Porcentaje de Palabras encontradas por tipo de sentimiento (bing) 13.5%
sentiment Porcentaje
negative 55%
positive 45%
Porcentaje de Palabras encontradas por tipo de sentimiento (nrc) 20.3%
sentiment Porcentaje
positive 18.1%
negative 15.4%
trust 11.9%
sadness 9.2%
anticipation 8.8%
joy 8.7%
anger 8.7%
fear 8.0%
disgust 5.7%
surprise 5.6%
Porcentaje de Palabras encontradas por tipo de sentimiento (loughran) 6.08%
sentiment Porcentaje
negative 53.0%
uncertainty 21.9%
positive 20.7%
litigious 4.4%

Score por Personaje

[1] “Analisis de Sentimientos del Personaje: JIM KURRING” [1] “Numero total de Palabras Unicas en el texto: 558”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5.263158 11.6%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 12.5%
sentiment Porcentaje
positive 52.08%
negative 47.92%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 16.1%
sentiment Porcentaje
positive 18.3%
negative 14.2%
trust 11.4%
sadness 10.0%
anticipation 9.5%
fear 9.3%
anger 7.9%
joy 7.9%
surprise 6.5%
disgust 5.1%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 7.17%
sentiment Porcentaje
negative 45.5%
uncertainty 24.7%
positive 19.5%
litigious 10.4%

[1] “Analisis de Sentimientos del Personaje: JIMMY” [1] “Numero total de Palabras Unicas en el texto: 412”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 4.586777 12.6%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 11.7%
sentiment Porcentaje
negative 63%
positive 37%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 16.7%
sentiment Porcentaje
negative 19.8%
positive 14.6%
sadness 13.8%
anger 10.7%
fear 9.1%
disgust 8.7%
trust 7.1%
joy 6.3%
anticipation 5.1%
surprise 4.7%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 5.83%
sentiment Porcentaje
negative 47.1%
uncertainty 35.3%
positive 17.6%

[1] “Analisis de Sentimientos del Personaje: CLAUDIA” [1] “Numero total de Palabras Unicas en el texto: 279”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 4.368932 14.7%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 12.9%
sentiment Porcentaje
negative 69.9%
positive 30.1%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 14%
sentiment Porcentaje
positive 16.7%
negative 15.5%
anticipation 10.7%
anger 8.9%
sadness 8.9%
trust 8.9%
fear 8.3%
joy 7.7%
surprise 7.7%
disgust 6.5%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 5.38%
sentiment Porcentaje
negative 36%
positive 36%
uncertainty 24%
litigious 4%

[1] “Analisis de Sentimientos del Personaje: FRANK” [1] “Numero total de Palabras Unicas en el texto: 785”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 4.192488 13.2%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 12.9%
sentiment Porcentaje
negative 56%
positive 44%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 18.6%
sentiment Porcentaje
positive 19.9%
negative 17.3%
trust 14.0%
anticipation 8.2%
joy 8.2%
anger 8.0%
sadness 7.4%
fear 6.6%
disgust 6.0%
surprise 4.3%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 4.71%
sentiment Porcentaje
negative 60.0%
uncertainty 21.8%
positive 18.2%

[1] “Analisis de Sentimientos del Personaje: PHIL” [1] “Numero total de Palabras Unicas en el texto: 330”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5.04 10.9%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 8.79%
sentiment Porcentaje
positive 54.55%
negative 45.45%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 13%
sentiment Porcentaje
positive 24.0%
trust 15.8%
negative 14.2%
sadness 8.7%
anticipation 8.2%
fear 7.1%
anger 6.6%
disgust 6.6%
joy 5.5%
surprise 3.3%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 3.33%
sentiment Porcentaje
uncertainty 42.9%
positive 28.6%
negative 21.4%
litigious 7.1%

[1] “Analisis de Sentimientos del Personaje: STANLEY” [1] “Numero total de Palabras Unicas en el texto: 215”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 6.139535 7.44%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 7.44%
sentiment Porcentaje
positive 73.7%
negative 26.3%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 10.2%
sentiment Porcentaje
positive 22.2%
joy 16.7%
anticipation 15.3%
trust 13.9%
negative 9.7%
anger 8.3%
surprise 6.9%
sadness 4.2%
disgust 1.4%
fear 1.4%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 1.86%
sentiment Porcentaje
negative 62.5%
uncertainty 37.5%

[1] “Analisis de Sentimientos del Personaje: DONNIE” [1] “Numero total de Palabras Unicas en el texto: 385”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5.281818 12.7%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 11.4%
sentiment Porcentaje
positive 55.1%
negative 44.9%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 17.1%
sentiment Porcentaje
positive 19.2%
joy 13.4%
negative 12.5%
trust 11.9%
anticipation 9.5%
anger 8.8%
sadness 7.0%
surprise 7.0%
fear 6.1%
disgust 4.6%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 5.19%
sentiment Porcentaje
negative 46.9%
positive 40.6%
uncertainty 12.5%

[1] “Analisis de Sentimientos del Personaje: LINDA” [1] “Numero total de Palabras Unicas en el texto: 305”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 3.319672 14.4%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 12.8%
sentiment Porcentaje
negative 74.8%
positive 25.2%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 14.4%
sentiment Porcentaje
positive 16.5%
negative 16.0%
fear 11.9%
sadness 10.3%
anger 9.3%
trust 9.3%
disgust 8.2%
anticipation 7.2%
joy 7.2%
surprise 4.1%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 4.26%
sentiment Porcentaje
negative 75.0%
litigious 12.5%
positive 8.3%
uncertainty 4.2%

[1] “Analisis de Sentimientos del Personaje: NARRATOR” [1] “Numero total de Palabras Unicas en el texto: 347”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 4.266667 8.65%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 7.49%
sentiment Porcentaje
negative 69.2%
positive 30.8%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 18.2%
sentiment Porcentaje
negative 17.0%
positive 13.5%
anger 10.8%
fear 10.3%
sadness 10.3%
trust 10.3%
anticipation 7.6%
surprise 7.6%
disgust 6.7%
joy 5.8%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 5.19%
sentiment Porcentaje
negative 66.7%
uncertainty 28.6%
litigious 4.8%

[1] “Analisis de Sentimientos del Personaje: ROSE” [1] “Numero total de Palabras Unicas en el texto: 148”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 4.384615 11.5%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 9.46%
sentiment Porcentaje
negative 64.7%
positive 35.3%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 11.5%
sentiment Porcentaje
positive 25.9%
negative 13.0%
sadness 13.0%
fear 11.1%
anger 9.3%
anticipation 9.3%
disgust 9.3%
surprise 3.7%
trust 3.7%
joy 1.9%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 4.05%
sentiment Porcentaje
negative 66.7%
positive 22.2%
uncertainty 11.1%

[1] “Analisis de Sentimientos del Personaje: EARL” [1] “Numero total de Palabras Unicas en el texto: 361”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 4.365591 14.4%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 11.4%
sentiment Porcentaje
negative 52.11%
positive 47.89%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 11.9%
sentiment Porcentaje
positive 15.2%
negative 14.7%
joy 13.7%
trust 13.3%
anticipation 9.5%
sadness 8.5%
anger 7.6%
disgust 7.6%
fear 6.2%
surprise 3.8%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 3.6%
sentiment Porcentaje
positive 57.1%
negative 23.8%
uncertainty 19.0%

[1] “Analisis de Sentimientos del Personaje: DIXON” [1] “Numero total de Palabras Unicas en el texto: 232”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 3.8 13.8%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 11.2%
sentiment Porcentaje
negative 67.6%
positive 32.4%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 20.7%
sentiment Porcentaje
positive 14.8%
anticipation 12.9%
anger 11.4%
negative 11.4%
trust 11.0%
surprise 10.5%
joy 9.0%
fear 7.1%
sadness 7.1%
disgust 4.8%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 3.02%
sentiment Porcentaje
positive 42.9%
negative 28.6%
uncertainty 28.6%

[1] “Analisis de Sentimientos del Personaje: MARCIE” [1] “Numero total de Palabras Unicas en el texto: 125”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 3.45 14.4%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 12%
sentiment Porcentaje
negative 77.8%
positive 22.2%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 12%
sentiment Porcentaje
negative 22.1%
anger 14.3%
positive 13.0%
fear 10.4%
trust 9.1%
anticipation 7.8%
disgust 6.5%
joy 6.5%
sadness 6.5%
surprise 3.9%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 4.8%
sentiment Porcentaje
negative 75.0%
litigious 12.5%
uncertainty 12.5%

[1] “Analisis de Sentimientos del Personaje: GWENOVIER” [1] “Numero total de Palabras Unicas en el texto: 176”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5.037037 9.09%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 9.66%
sentiment Porcentaje
negative 54.17%
positive 45.83%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 13.1%
sentiment Porcentaje
positive 26.4%
trust 18.4%
negative 13.8%
anticipation 12.6%
sadness 10.3%
joy 6.9%
anger 3.4%
fear 3.4%
disgust 2.3%
surprise 2.3%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 5.68%
sentiment Porcentaje
negative 83.3%
uncertainty 16.7%

[1] “Analisis de Sentimientos del Personaje: RICK” [1] “Numero total de Palabras Unicas en el texto: 155”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 2.826087 8.39%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 7.1%
sentiment Porcentaje
negative 70%
positive 30%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 7.74%
sentiment Porcentaje
positive 24.2%
negative 15.2%
sadness 12.1%
anticipation 9.1%
fear 9.1%
joy 9.1%
trust 9.1%
anger 6.1%
disgust 3.0%
surprise 3.0%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 3.23%
sentiment Porcentaje
negative 100%

[1] “Analisis de Sentimientos del Personaje: THURSTON” [1] “Numero total de Palabras Unicas en el texto: 151”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5.157895 10.6%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 13.9%
sentiment Porcentaje
negative 50%
positive 50%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 15.9%
sentiment Porcentaje
positive 22.7%
joy 17.3%
negative 16.0%
trust 13.3%
sadness 9.3%
anger 5.3%
anticipation 5.3%
fear 5.3%
disgust 2.7%
surprise 2.7%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 9.93%
sentiment Porcentaje
uncertainty 47.1%
negative 41.2%
litigious 5.9%
positive 5.9%

[1] “Analisis de Sentimientos del Personaje: WORM” [1] “Numero total de Palabras Unicas en el texto: 97”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 3.384615 11.3%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 9.28%
sentiment Porcentaje
negative 71.4%
positive 28.6%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 8.25%
sentiment Porcentaje
negative 17.9%
anger 16.1%
sadness 14.3%
disgust 12.5%
fear 12.5%
positive 10.7%
trust 5.4%
anticipation 3.6%
joy 3.6%
surprise 3.6%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 1.03%
sentiment Porcentaje
uncertainty 100%

[1] “Analisis de Sentimientos del Personaje: RICHARD” [1] “Numero total de Palabras Unicas en el texto: 131”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 3.782609 12.2%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 9.92%
sentiment Porcentaje
negative 56.2%
positive 43.8%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 9.92%
sentiment Porcentaje
negative 26.7%
trust 16.7%
anger 13.3%
disgust 13.3%
anticipation 6.7%
fear 6.7%
positive 6.7%
joy 3.3%
sadness 3.3%
surprise 3.3%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 3.82%
sentiment Porcentaje
uncertainty 50.0%
negative 33.3%
positive 16.7%

[1] “Analisis de Sentimientos del Personaje: CYNTHIA” [1] “Numero total de Palabras Unicas en el texto: 116”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5 7.76%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 6.9%
sentiment Porcentaje
positive 54.55%
negative 45.45%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 8.62%
sentiment Porcentaje
anticipation 20%
negative 20%
fear 15%
positive 15%
sadness 15%
anger 5%
surprise 5%
trust 5%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 5.17%
sentiment Porcentaje
negative 85.7%
uncertainty 14.3%

[1] “Analisis de Sentimientos del Personaje: GWEN” [1] “Numero total de Palabras Unicas en el texto: 86”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 3.857143 8.14%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 10.5%
sentiment Porcentaje
negative 50%
positive 50%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 14%
sentiment Porcentaje
trust 28.6%
positive 23.8%
joy 14.3%
negative 14.3%
anticipation 4.8%
fear 4.8%
sadness 4.8%
surprise 4.8%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 4.65%
sentiment Porcentaje
negative 100%

[1] “Analisis de Sentimientos del Personaje: CHAD” [1] “Numero total de Palabras Unicas en el texto: 96”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5.25 6.25%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 7.29%
sentiment Porcentaje
positive 60%
negative 40%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 5.21%
sentiment Porcentaje
negative 19.0%
sadness 14.3%
trust 14.3%
anger 9.5%
disgust 9.5%
fear 9.5%
joy 9.5%
positive 9.5%
anticipation 4.8%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 1.04%
sentiment Porcentaje
negative 100%

[1] “Analisis de Sentimientos del Personaje: JIMMY GATOR” [1] “Numero total de Palabras Unicas en el texto: 200”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 6 6.5%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 10%
sentiment Porcentaje
positive 73.1%
negative 26.9%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 15.5%
sentiment Porcentaje
positive 23.1%
joy 13.7%
surprise 13.7%
trust 12.8%
anticipation 11.1%
sadness 7.7%
anger 6.0%
fear 5.1%
negative 4.3%
disgust 2.6%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 2%
sentiment Porcentaje
negative 71.4%
positive 28.6%

[1] “Analisis de Sentimientos del Personaje: SOLOMON” [1] “Numero total de Palabras Unicas en el texto: 137”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 4.095238 10.9%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 6.57%
sentiment Porcentaje
negative 76.9%
positive 23.1%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 11.7%
sentiment Porcentaje
positive 30.5%
trust 20.3%
anticipation 11.9%
negative 11.9%
surprise 8.5%
joy 6.8%
anger 5.1%
sadness 5.1%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 2.19%
sentiment Porcentaje
negative 80%
positive 20%

[1] “Analisis de Sentimientos del Personaje: BURT” [1] “Numero total de Palabras Unicas en el texto: 83”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 3 9.64%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 15.7%
sentiment Porcentaje
negative 57.9%
positive 42.1%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 12%
sentiment Porcentaje
negative 31.6%
sadness 15.8%
anger 10.5%
fear 10.5%
positive 10.5%
anticipation 5.3%
disgust 5.3%
surprise 5.3%
trust 5.3%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 6.02%
sentiment Porcentaje
negative 100%

[1] “Analisis de Sentimientos del Personaje: JULIA” [1] “Numero total de Palabras Unicas en el texto: 88”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5.857143 5.68%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 2.27%
sentiment Porcentaje
positive 100%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 5.68%
sentiment Porcentaje
trust 30%
anticipation 20%
fear 20%
joy 10%
positive 10%
surprise 10%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 4.55%
sentiment Porcentaje
uncertainty 50%
negative 25%
positive 25%

[1] “Analisis de Sentimientos del Personaje: KLIGMAN” [1] “Numero total de Palabras Unicas en el texto: 117”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5.1 7.69%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 8.55%
sentiment Porcentaje
positive 54.55%
negative 45.45%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 15.4%
sentiment Porcentaje
positive 19.6%
trust 14.3%
anticipation 12.5%
anger 10.7%
fear 10.7%
joy 8.9%
negative 8.9%
sadness 5.4%
surprise 5.4%
disgust 3.6%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 5.13%
sentiment Porcentaje
litigious 57.1%
negative 42.9%

[1] “Analisis de Sentimientos del Personaje: PINK DOT GIRL” [1] “Numero total de Palabras Unicas en el texto: 39”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 6.166667 7.69%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 2.56%
sentiment Porcentaje
positive 100%

Table: Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 0%

sentiment Porcentaje ———- ————

Table: Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 0%

sentiment Porcentaje ———- ————

[1] “Analisis de Sentimientos del Personaje: PAULA” [1] “Numero total de Palabras Unicas en el texto: 95”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 3.266667 10.5%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 8.42%
sentiment Porcentaje
positive 54.55%
negative 45.45%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 7.37%
sentiment Porcentaje
negative 19.4%
disgust 16.7%
anger 13.9%
anticipation 8.3%
positive 8.3%
sadness 8.3%
trust 8.3%
fear 5.6%
joy 5.6%
surprise 5.6%

Table: Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 0%

sentiment Porcentaje ———- ————

[1] “Analisis de Sentimientos del Personaje: DOC” [1] “Numero total de Palabras Unicas en el texto: 59”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 6 5.08%

Table: Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 0%

sentiment Porcentaje ———- ————

Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 5.08%
sentiment Porcentaje
positive 44.4%
trust 33.3%
joy 22.2%

Table: Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 0%

sentiment Porcentaje ———- ————

[1] “Analisis de Sentimientos del Personaje: AVI” [1] “Numero total de Palabras Unicas en el texto: 55”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5 9.09%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 7.27%
sentiment Porcentaje
negative 50%
positive 50%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 14.5%
sentiment Porcentaje
positive 23.1%
trust 19.2%
anticipation 15.4%
anger 7.7%
fear 7.7%
joy 7.7%
negative 7.7%
surprise 7.7%
sadness 3.8%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 3.64%
sentiment Porcentaje
negative 50%
positive 50%

[1] “Analisis de Sentimientos del Personaje: DR. LANDON” [1] “Numero total de Palabras Unicas en el texto: 157”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5.96 10.2%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 10.2%
sentiment Porcentaje
positive 69.6%
negative 30.4%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 10.2%
sentiment Porcentaje
positive 22.4%
sadness 22.4%
negative 14.3%
fear 12.2%
anticipation 8.2%
trust 6.1%
anger 4.1%
disgust 4.1%
joy 4.1%
surprise 2.0%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 4.46%
sentiment Porcentaje
positive 54.5%
uncertainty 27.3%
negative 18.2%

[1] “Analisis de Sentimientos del Personaje: JANET” [1] “Numero total de Palabras Unicas en el texto: 52”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 3 5.77%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 5.77%
sentiment Porcentaje
negative 66.7%
positive 33.3%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 7.69%
sentiment Porcentaje
positive 42.9%
negative 28.6%
trust 28.6%
Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 3.85%
sentiment Porcentaje
negative 100%

[1] “Analisis de Sentimientos del Personaje: MIDDLE AGED GUY” [1] “Numero total de Palabras Unicas en el texto: 40”

Escala de Sentimientos entre negativos y positivos: afinn
Descripcion Score % Founded Words
Entre 0 (negativo) y 10 (positivo) 5.333333 7.5%
Porcentaje de Palabras encontradas por tipo de sentimiento ( bing ) 2.5%
sentiment Porcentaje
positive 100%
Porcentaje de Palabras encontradas por tipo de sentimiento ( nrc ) 2.5%
sentiment Porcentaje
joy 33.3%
positive 33.3%
trust 33.3%

Table: Porcentaje de Palabras encontradas por tipo de sentimiento ( loughran ) 0%

sentiment Porcentaje ———- ————

Score por Personaje en el tiempo

Top 10 Personajes

Dialogos cúspide por Top 10 Personajes: magnolia
Personaje Min_Max Dialogo
JIM KURRING MIN You mind if I check things back here?
JIM KURRING MAX YOU’RE OK…you’re gonna be ok….
JIMMY MIN She went crazy. She went crazy, Rose.
JIMMY MAX Imagine you are attending a jam session of classical composers and they have each done an arrangment of the classic favorite, “Whispering.” Here are three variations on the theme, as three classic composer’s might have written it – you are to name the composer. The First:
CLAUDIA MIN I’m sorry.
CLAUDIA MAX Did you ever go out with someone and just….lie….question after question, maybe you’re trying to make yourself look cool or better than you are or whatever, or smarter or cooler and you just – not really lie, but maybe you just don’t say everything –
FRANK MIN If you feel, made to feel like you need them, like – like you can’t live if you’re without them or you need, what? They’re pussy? They’re love? Fuck that. Self Sufficient, gents. That’s the truth. What you are – we are – you need them for what? To fucking make you a piece of snot rag? A puppett? huh? Hear them bitch and moan? bitch and moan – and we’re taught one thing – go the other way – there is No Excuse I will give you, I’m not gonna apologize – I’m not gonna apologize for my NEED my DESIRE…my, the things that I need as a man to feel comfortable… You understand? You understand? You need to say something, “my mommy hit me or daddy hit me or didn’t let me play soccer, so now I make mistakes, cause a that – something, so now I piss and shit on it and do this.” Bullshit. I’m sorry. ok. yeah. no. fuck. go. fuck. alright. go make a new mistake. maybe not, I dunno…fuck….
FRANK MAX I wouldn’t want that to be misunderstood: My enrollment was totally unoffical because I was, sadly, unable to afford tuition up there. But there were three wonderful men who were kind enough to let me sit in on their classes, and they’re names are: Macready, Horn and Langtree among others. I was completely independent financially, and like I said: One Sad Sack A Shit. So what we’re looking at here is a true rags to riches story and I think that’s what most people respond to in “Seduce,” And At The End Of The Day? Hey – it may not even be about picking up chicks and sticking your cock in it – it’s about finding What You Can Be In This World. Defining It. Controling It and saying: I will take what is mine. You just happen to get a blow job out of it, then hey-what-the-fuck- why-not? he.he.he.
PHIL MIN You wanna call him on the phone? We can call him, I can dial the phone if you can remember the number –
PHIL MAX Thank you, Chad, and good luck to you and your mother –
STANLEY MIN I think that you have to be nicer to me.
STANLEY MAX I’m fine. I’m fine, I just wanna keep playing –
DONNIE MIN My teeff…my teeef….
DONNIE MAX My name is Donnie Smith and I have lot’s of love to give.
EARL MIN No, no, the grade…the grade that you’re in?
EARL MAX “…it’s not going to stop ’till you wise up…”
LINDA MIN listen…listen to me now, Phil: I’m sorry, sorry I slapped your face. …because I don’t know what I’m doing… …I don’t know how to do this, y’know? You understand? y’know? I…I’m…I do things and I fuck up and I fucked up….forgive me, ok? Can you just…
LINDA MAX I’m listening. I’m getting better.
NARRATOR MIN – added to this, Mr. Hansen’s tortured life met before with Delmer Darion just two nights previous –
NARRATOR MAX So Fay Barringer was charged with the murder of her son and Sydney Barringer noted as an accomplice in his own death…

Top 4 Parejas

Dialogos cúspide por Top 4 Parejas: magnolia
Parejas Min_Max Dialogo
JIM KURRING_CLAUDIA MIN You mind if I check things back here?
JIM KURRING_CLAUDIA MAX ok.
JIMMY_STANLEY MIN I don’t mean to cry, I’m sorry.
JIMMY_STANLEY MAX Imagine you are attending a jam session of classical composers and they have each done an arrangment of the classic favorite, “Whispering.” Here are three variations on the theme, as three classic composer’s might have written it – you are to name the composer. The First:
PHIL_EARL MIN – it’s not him. it’s not him. He’s the fuckin’ asshole…Phil..c’mere…
PHIL_EARL MAX …ah…maybe…yeah…she’s a good one…
FRANK_PHIL MIN When they put me on hold, to talk to you…they play the tapes. I mean: I’d seen the commercials and heard about you, but I’d never heard the tapes ….
FRANK_PHIL MAX I just…he was…but I gave him, I just had to give him a small dose of liquid morphine. He hasn’t been able to swallow the morphine pills so we now, I just had to go to the liquid morphine… For the pain, you understand?

Reglas de Asociación entre palabras (Market Basket)

Toda la pelicula

## [1] "Lift Promedio de las Reglas de Asociacion: 34.9702084954971"
## [1] "Desviación estandar del Lift de las Reglas de Asociacion: 22.9809027673556"
## [1] "Deciles del Lift : "
##       10%       20%       30%       40%       50%       60%       70% 
##   6.17289  15.84375  25.50000  33.06522  39.00000  39.00000  39.00000 
##       80%       90%      100% 
##  39.00000  47.71765 304.20000

Datos del Histograma: Lift Pelicula: magnolia
Numero de Dialogos Lift Minimo Lift Maximo
11,680 -5 5
15,356 5 16
13,722 16 26
15,480 26 37
57,022 37 47
11,860 47 58
## [1] "Leverage Promedio de las Reglas de Asociacion: 0.00676980280962111"
## [1] "Desviación estandar del Leverage de las Reglas de Asociacion: 0.00508006266842479"
## [1] "Deciles del Leverage : "
##         10%         20%         30%         40%         50%         60% 
## 0.003105763 0.003184434 0.003222472 0.004463049 0.005124834 0.005149472 
##         70%         80%         90%        100% 
## 0.008968459 0.009609063 0.010890271 0.094336532

Datos del Histograma: Leverage pelicula: magnolia
Numero de Dialogos Leverage Minimo Leverage Maximo
2,036 -0.0016 0.0016
54,550 0.0016 0.0049
31,344 0.0049 0.0081
38,338 0.0081 0.011
2,544 0.011 0.015
406 0.015 0.018

Top 10 Personajes

Top 4 Parejas

Analisis de Relaciones entre Personajes (Pagerank)

Pagerank: Magnolia.

Pagerank: Magnolia.